Architect and Optimize Software Projects

The skill provides three automated scripts for architecture work: scripts/architecture_diagram_generator.py <project-path> [options] for generating architecture diagrams, scripts/project_architect.py <target-path> [--verbose] for comprehensive project analysis and optimization recommendations, and scripts/dependency_analyzer.py [arguments] [options] for dependency analysis. It also ships three reference documents: references/architecture_patterns.md for patterns, code examples, and anti-patterns; references/system_design_workflows.md for step-by-step design workflows, optimization strategies, and tool integrations; and references/tech_decision_guide.md for technology-stack decisions, configuration examples, and security/scalability guidance - the same file it points to for troubleshooting.

The named tech stack spans TypeScript, JavaScript, Python, Go, Swift, and Kotlin for languages; React, Next.js, React Native, and Flutter on the frontend; Node.js, Express, GraphQL, and REST APIs on the backend; PostgreSQL, Prisma, NeonDB, and Supabase for data; Docker, Kubernetes, Terraform, GitHub Actions, and CircleCI for DevOps; and AWS, GCP, and Azure for cloud. The development workflow is: install dependencies and configure the environment, run project_architect.py to get recommendations and apply fixes, then implement the patterns documented in the three reference files. Best practices are grouped into code quality (established patterns, comprehensive tests, documented decisions), performance (measure before optimizing, appropriate caching, monitor in production), security (validate inputs, parameterized queries, proper authentication, updated dependencies), and maintainability (clear code, consistent naming, simplicity).

Setup, quoted verbatim from the source:

### Install dependencies
npm install
### or
pip install -r requirements.txt

### Configure environment
cp .env.example .env

Common commands cover both development and analysis: npm run dev, npm run build, npm run test, and npm run lint for the app itself; python scripts/project_architect.py . and python scripts/dependency_analyzer.py --analyze for analysis; and docker build -t app:latest ., docker-compose up -d, and kubectl apply -f k8s/ for deployment. For troubleshooting, the skill points to the comprehensive troubleshooting section inside references/tech_decision_guide.md, and for further help it directs the agent to review the reference documentation, check script output messages, consult the tech-stack documentation, and review error logs rather than guessing at a fix.

Use it when architecting or reviewing a project's structure and needing diagram generation, structural analysis, or dependency review backed by documented patterns and a defined tech stack. It is not a substitute for environment-specific validation, testing, or expert review, and it stops for clarification when required inputs, permissions, safety boundaries, or success criteria are missing.

Input is a project or target path passed to one of the three scripts. Output is an architecture diagram, a project analysis with performance metrics and recommendations, or a dependency analysis report, plus the reference documentation to act on the findings.

Architects and senior engineers who need diagram generation, project-structure analysis, and dependency review as scripted, repeatable tools rather than manual, one-off reviews.